# Copyright (c) 2009 IW.
# All rights reserved.
#
# Author: liuguiyang <liuguiyangnwpu@gmail.com>
# Date:   2017/10/17

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from matplotlib import pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

res_details_dict = {'pl80': [[0.0, 0.0, 0.27414435784479835, 1.0], [0.0, 0.125, 0.9623655913978495, 0.9930651872399445], [0.0, 0.25, 0.9982046678635548, 0.9858156028368794], [0.0, 0.375, 0.9976958525345622, 0.9931192660550459], [0.0, 0.5, 1.0, 0.9891304347826086], [0.0, 0.625, 1.0, 0.9880597014925373], [0.0, 0.75, 1.0, 0.9775641025641025], [0.0, 0.875, 1.0, 0.9691780821917808], [0.0, 1.0, 0, 0], [0.125, 0.0, 0.2738054896645205, 0.9987639060568603], [0.125, 0.125, 0.9623655913978495, 0.9930651872399445], [0.125, 0.25, 0.9982046678635548, 0.9858156028368794], [0.125, 0.375, 0.9976958525345622, 0.9931192660550459], [0.125, 0.5, 1.0, 0.9891304347826086], [0.125, 0.625, 1.0, 0.9880597014925373], [0.125, 0.75, 1.0, 0.9775641025641025], [0.125, 0.875, 1.0, 0.9691780821917808], [0.125, 1.0, 0, 0], [0.25, 0.0, 0.2738054896645205, 0.9987639060568603], [0.25, 0.125, 0.9623655913978495, 0.9930651872399445], [0.25, 0.25, 0.9982046678635548, 0.9858156028368794], [0.25, 0.375, 0.9976958525345622, 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# [iou, prob, precision, recall]
iou_limit, prob_limit = 0.75, 0.75
for sign_name, sign_detail in res_details_dict.items():
    print(sign_name)
    for item in sign_detail:
        if item[0] == iou_limit and item[1] == prob_limit:
            print(item)

# fig = plt.figure(figsize=(6, 4))
# ax = Axes3D(fig)
#
# sub_sign_info = res_details_dict["pl80"]
# sign_np = np.array(sub_sign_info)
# precision_np = sign_np[:, 3]
# # IOU
# X = np.linspace(0, 1, 9)
# # PROB
# Y = np.linspace(0, 1, 9)
# X, Y = np.meshgrid(X, Y)
# Z = precision_np.reshape((9, 9)).T
# # print(Z)
#
# surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='coolwarm',
#                 linewidth=1, antialiased=True)
# fig.colorbar(surf, shrink=0.5, aspect=5)
# ax.set_xlabel("IOU")
# ax.set_ylabel("PROB")
# ax.set_zlabel("Recall")
# # plt.savefig("percision.png", format='png')
# plt.show()

# import pandas as pd
# import csv
# 
# with open("ssd.run.log", "r") as log_reader:
#     cur_idx = 0
#     res_list = list()
#     for line in log_reader:
#         cur_idx += 1
#         info = line.strip().split("]")[-1].strip()
#         if cur_idx % 3 == 1:
#             res_list.append(list())
#             iteration, loss_info = info.split(",")
#             iter_cnt = iteration.split(" ")[-1].strip()
#             loss_val = loss_info.split("=")[-1].strip()
#             res_list[-1].append(iter_cnt)
#             res_list[-1].append(loss_val)
#         if cur_idx % 3 == 2:
#             mbox_loss = info.split(":")[-1].strip().split("(")[0].strip().split(
#                 "=")[-1].strip()
#             res_list[-1].append(mbox_loss)
#         if cur_idx % 3 == 0:
#             lr_val = info.split(",")[-1].strip().split("=")[-1].strip()
#             res_list[-1].append(lr_val)
#     # print(res_list)

# sub_res_list = []
# for i in range(len(res_list)):
#     if i % 50 == 0:
#         sub_res_list.append(res_list[i])
#         print("{},{},{}".format(
#             res_list[i][0], res_list[i][1], res_list[i][2], res_list[i][3]))
# 
# with open("a.csv", "w") as h:
#     writer = csv.writer(h)
#     writer.writerows(sub_res_list)
# 
# pd_info = pd.DataFrame(
#     data=sub_res_list, columns=["iter", "loss", "mbox_loss"])
# pd_info_iter = pd_info.set_index("iter")
# pd_info_iter = pd_info_iter.apply(pd.to_numeric)
# pd_info_iter.plot()
# plt.show()

# import os
# import argparse
# 
# def dealwith_image_list(src_path, out_path, frame_nums=16):
#     if not os.path.exists(src_path):
#         raise IOError("Not %s path" % src_path)
#     res_list = list()
#     with open(src_path, "r") as reader:
#         for line in reader:
#             line = line.strip()
#             image_dir, image_label = line.split()
#             img_nums = len(os.listdir(image_dir))
#             for idx in range(1, img_nums, frame_nums):
#                 if idx + frame_nums > img_nums:
#                     idx = img_nums - frame_nums
#                 res_list.append([image_dir, idx, image_label])
#     with open(out_path, "w") as writer:
#         for item in res_list:
#             writer.write("{} {} {}\n".format(item[0], item[1], item[2]))
# 
# if __name__ == '__main__':
#     parser = argparse.ArgumentParser()
#     parser.add_argument("-i", "--input", help="input source path")
#     parser.add_argument("-o", "--output", help="output path")
#     parser.add_argument("-f", "--fps", default=16, help="frame number in each group")
#     args = parser.parse_args()
#     src_path = args.input
#     out_path = args.output
#     frame_nums = args.fps
#     dealwith_image_list(src_path, out_path, frame_nums)